A wireless communication network can be viewed as a collection of nodes, located in some domain, which can in turn be transmitters or receivers (depending on the network considered, nodes may be mobile users, base stations in a cellular network, access points of a WiFi mesh etc.). At a given time, several nodes transmit simultaneously, each toward its own receiver. Each transmitter–receiver pair requires its own wireless link. The signal received from the link transmitter may be jammed by the signals received from the other transmitters. Even in the simplest MODEL where the signal power radiated from a point decays in an isotropic way with Euclidean distance, the geometry of the locations of the nodes plays a key role since it determines the signal to interference and noise ratio (SINR) at each receiver and hence the possibility of establishing simultaneously this collection of links at a given bit rate. The interference seen by a receiver is the sum of the signal powers received from all transmitters, except its own transmitter.
标签： Stochastic Geometry Networks Wireless Volume and II
上传时间： 2020-06-01
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Under the Energy Independence and Security Act of 2007 (EISA), the National Institute of Standards and Technology (NIST) was assigned “primary responsibility to coordinate development of a framework that includes protocols and MODEL standards for information management to achieve interoperability of Smart Grid devices and systems…” [EISA Section 1305]. 35 This responsibility comes at a time when the electric power grid and electric power industry are undergoing the most dramatic transformation in many decades. Very significant investments are being made by industry and the federal government to modernize the power grid. To realize the full benefits of these investments—and the continued investments forecast for the coming decades—there is a continued need to establish effective smart grid 36 standards and protocols for interoperability.
标签： Framework Roadmap NIST and
上传时间： 2020-06-07
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Have you ever looked at some gadget and wondered how it really worked? Maybe it was a remote control boat, the system that controls an elevator, a vending machine, or an electronic toy? Or have you wanted to create your own robot or electronic signals for a MODEL railroad, or per- haps you’d like to capture and analyze weather data over time? Where and how do you start?
标签： Introduction Workshop Hands-On Arduino
上传时间： 2020-06-09
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This introductory chapter is devoted to reviewing the fundamental ideas of control from a multivariable point of view. In some cases, the mathematics and operations on systems (MODELling, pole placement, etc.), as previously treated in introductory courses and textbooks, convey to the readers an un- realistic image of systems engineering. The simplifying assumptions, simple examples and “perfect” MODEL set-up usually used in these scenarios present the control problem as a pure mathematical problem, sometimes losing the physical meaning of the involved concepts and operations. We try to empha- sise the engineering implication of some of these concepts and, before entering into a detailed treatment of the different topics, a general qualitative overview is provided in this chapter.
标签： MultivariableControlSystems
上传时间： 2020-06-10
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Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge MODEL sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_Deep_Learning Algorithms
上传时间： 2020-06-10
上传用户：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately MODEL nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
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Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning MODELs and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable MODELs such as decision trees, decision rules and linear regression. Later chapters focus on general MODEL- agnosticmethodsforinterpretingblackboxMODELslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
标签： interpretable-machine-learning
上传时间： 2020-06-10
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The present era of research and development is all about interdisciplinary studies attempting to better comprehend and MODEL our understanding of this vast universe. The fields of biology and computer science are no exception. This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine—from storing enormous amounts of biological data to solving complex biological problems and enhancing the treatment of various diseases.
上传时间： 2020-06-10
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Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a MODEL, and learning.
上传时间： 2020-06-10
上传用户：shancjb
modbus-demo, Public M_3W_D(50) As Long Public M_4W_D(500) As Long Public M_0W_B(100) As Long Public M_1W_B(100) As Long Public PLC_B(50) As Single Public PLC_C(50) As Long Public T1t(500) As Long Public M_3x(500) As Long Public M_4x(500) As Long Public MODEL As Long Public Party As Long
标签： modbusdemo
上传时间： 2020-11-09
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